End-diastolic and end-systolic volume from the left ventricular angiogram: how accurate is visual frame selection? Comparison between visual and semi-automated comnputer-assisted analysis

Left ventricular volume: an optimal parameter to detect systolic dysfunction on prospectively triggered 64-multidetector row computed tomography: another step towards reducing radiation exposure

In this study, we define the correlation between LV volumes (both LV end-diastolic volume [LVEDV] and LV end-systolic volume [LVESV]) and ejection fraction (EF) on 64 slice multi-detector computed tomography (MDCT). We also determine the accuracy of all the LV volume (LVV) parameters to detect LV systolic dysfunction (LVSD) and investigate the feasibility of using LVV as a surrogate of LVSD on prospectively gated imaging to prevent the radiation exposure of retrospective imaging. 568 patients undergoing 64-detector MDCT were divided into 2 groups: Group 1—subjects without any heart disease and LVEF ≥ 50%; and Group 2—patients with coronary artery disease and LVEF < 50% (defined as LVSD). The LVV (LV cavity only) and Total LV volume (cavity + LV mass) at end-systole and end-diastole (LVESV, Total LVESV, LVEDV and Total LVEDV) were measured. The upper limit values (mean + 2 SD) of all LVV parameters in Group 1 were used as the reference criterion to diagnose LVSD in Group 2. An exponential correlation was found between LVEF and all the LVV parameters. The specificity to detect LVSD in Group 2 was >90% and the sensitivity was 88.9, 83.3, 61.3 and 74.9% by using LVESV, Total LVESV, LVEDV and Total LVEDV, respectively. Systolic and diastolic LV volumes had a high correlation with LVEF and a high accuracy to detect LVSD. Thus, on prospectively triggered imaging, ventricular volumes can predict patients with reduced LVEF, and appropriate referrals can be made.

A Novel Two-Dimensional Echocardiographic Image Analysis System Using Artificial Intelligence-Learned Pattern Recognition for Rapid Automated Ejection Fraction

OBJECTIVES

We sought to test the hypothesis that a novel 2-dimensional echocardiographic image analysis system using artificial intelligence-learned pattern recognition can rapidly and reproducibly calculate ejection fraction (EF).

BACKGROUND

Echocardiographic EF by manual tracing is time consuming, and visual assessment is inherently subjective.

METHODS

We studied 218 patients (72 female), including 165 with abnormal left ventricular (LV) function. Auto EF incorporated a database trained on >10,000 human EF tracings to automatically locate and track the LV endocardium from routine grayscale digital cineloops and calculate EF in 15 s. Auto EF results were independently compared with manually traced biplane Simpson's rule, visual EF, and magnetic resonance imaging (MRI) in a subset.

RESULTS

Auto EF was possible in 200 (92%) of consecutive patients, of which 77% were completely automated and 23% required manual editing. Auto EF correlated well with manual EF (r = 0.98; 6% limits of agreement) and required less time per patient (48 +/- 26 s vs. 102 +/- 21 s; p < 0.01). Auto EF correlated well with visual EF by expert readers (r = 0.96; p < 0.001), but interobserver variability was greater (3.4 +/- 2.9% vs. 9.8 +/- 5.7%, respectively; p < 0.001). Visual EF was less accurate by novice readers (r = 0.82; 19% limits of agreement) and improved with trainee-operated Auto EF (r = 0.96; 7% limits of agreement). Auto EF also correlated with MRI EF (n = 21) (r = 0.95; 12% limits of agreement), but underestimated absolute volumes (r = 0.95; bias of -36 +/- 27 ml overall).

CONCLUSIONS

Auto EF can automatically calculate EF similarly to results by manual biplane Simpson's rule and MRI, with less variability than visual EF, and has clinical potential.

Left ventricular end-diastolic pressure is a predictor of mortality in cardiac surgery independently of left ventricular ejection fraction

BACKGROUND

Several risk factors have been shown to increase mortality in cardiac surgery. However, the importance of left ventricular end-diastolic pressure (LVEDP) as an independent risk factor before cardiac surgery is unclear. Method. This observational study investigated 3024 consecutive adult patients who underwent cardiac surgical procedures at the Montreal Heart Institute from 1996 to 2000. The primary outcome was in-hospital mortality with 99 deaths (3.3%) among these patients.

RESULTS

Of the 35 variables subjected to univariate analysis, 23 demonstrated a significant association with mortality. Stepwise multivariate logistic regression identified LVEDP as an independent predictor of mortality after cardiac surgery. The area under the receiver operating characteristic curve of the model predicting mortality was 0.85.

CONCLUSIONS

Elevated LVEDP is an independent predictor of mortality in cardiac surgery. This variable is independent of left ventricular ejection fraction.